Single-cell analysis in Python. Scales to >100M cells.
- machine-learning
- data-science
- visualize-data
- transcriptomics
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Single-cell analysis in Python. Scales to >100M cells.
This package contains deep learning models and related scripts for RoseTTAFold
Scientific workflow engine designed for simplicity & scalability. Trivially transition between one off use cases to massive scale production environments
Foundation Models for Genomics & Transcriptomics
Spatial Single Cell Analysis in Python
[ICLR 2024] DNABERT-2: Efficient Foundation Model and Benchmark for Multi-Species Genome
Haplotype-aware CNV analysis from single-cell RNA-seq, ATAC-seq, and multiome
Probabilistic cell segmentation for in situ spatial transcriptomics
Data visualisation web components for the life sciences.
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
BulkFormer: A large-scale foundation model for human bulk transcriptomes
Run scripts, explore your genome, and get personalized explanations — all in natural dialogue.
Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics
MCP server for spatial transcriptomics analysis through natural language interfaces.
Predicting Cell Health with Morphological Profiles
Deciphering cell-cell communication from spatially resolved transcriptomic data at single-cell resolution with subgraph-based attentional graph neural network
Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics
Vendor-agnostic, FHIR-Genomics-aligned API contract for cross-vendor NGS interpretation. JSON Schema + TypeScript + Python pydantic.